Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

Corporate social responsibility (CSR) is an important concept of modern economic theory. In the last few decades, it has become an increasingly popular marketing tool used by companies. Consumers too want to see more CSR activities, especially those focused on environmental protection. The petroleum industry produces both toxic and non-toxic waste at almost all stages of production. While petroleum companies satisfy market demand, they also want to meet consumers’ moral and ethical demands. In this light, CSR has become vital for the development of industry. This paper looks at CSR in the petroleum industry, and its effect on customer satisfaction and subsequently toward the customer repurchase intention in Malaysia. The starting point of this paper is the Stakeholder Theory. It then examines CSR endeavors within the oil and gas sector and its link to customer repurchase intentions. It also looks at the established hypotheses between the activities of CSR (Economic Responsibility, Legal Responsibility, Ethical Responsibility, Philanthropic Responsibility), customer satisfaction and repurchase intention. This paper aims to learn about the customer’s sense of fulfilment with the CSR activities, and what could be the reaction base on the customer’s expectation.

This research aims to develop a Synergy Learning Model in the context of science learning.  This research was conducted at Islamic Junior High School, Madrasah Tsanawiyah Negeri 2 Medan, involving 64 students of Grade 7 as the research subject. The method used in this research refers to the development research approach (R&D). In collecting the data, the research employed test and non-test techniques. The results prove that the Synergy learning model developed is effective in improving student learning outcomes. This is evident through the t-test statistical test where the t-count of 4.26 is higher than the t-table of 1.99. In addition, the level of practicality with a score of 3.39 is categorized as practical. This learning model emphasizes the learning process that supports the development of science skills and develops students' competencies in planning, collaborating, and critically reflecting. The findings of this study contribute to pedagogical practices and literature in the field of science learning.

While the notion of the smart city has grown in popularity, the backlash against smart urban infrastructure in the context of changing state-public relations has seldom been examined. This article draws on the case of Hong Kong’s smart lampposts to analyse the emergence of networked dissent against smart urban infrastructure during a period of unrest. Deriving insights from critical data studies, dissentworks theory, and relevant work on networked activism, the article illustrates how a smart urban infrastructure was turned into both a source and a target of popular dissent through digital mediation and politicisation. Drawing on an interpretive analysis of qualitative data collected from multiple digital platforms, the analysis explicates the citizen curation of socio-technic counter-imaginaries that constituted a consent of dissent in the digital realm, and the creation and diffusion of networked action repertoires in response to a changing political opportunity structure. In addition to explicating the words and deeds employed in this networked dissent, this article also discusses the technopolitical repercussions of this dissent for the city’s later attempts at data-based urban governance, which have unfolded at the intersections of urban techno-politics and local contentious politics. Moving beyond the common focus on neoliberal governmentality and its limits, this article reveals the underexplored pitfalls of smart urban infrastructure vis-à-vis the shifting socio-political landscape of Hong Kong, particularly in the digital age.

This study examines the development and influence of the international anti-corruption regime, utilizing Critical Discourse Analysis (CDA) to dissect the discursive practices that shape perceptions of corruption and the strategies employed to combat it. Our analysis reveals how Western institutional entrepreneurs play a pivotal role in defining corruption predominantly as bribery and governance failures, underpinned by a neoliberal ideology that prescribes societal norms and identifies corrupt practices. By exploring the mechanisms through which this ideology is propagated, the research enriches institutional entrepreneurship theory and highlights the neoliberal foundations of current anti-corruption efforts. This study not only enhances our understanding of the institutional frameworks that govern anti-corruption discourse but also demonstrates how discourse legitimizes certain ideologies while marginalizing others. The findings offer practical tools for altering power dynamics, promoting equitable participation, and addressing the imbalanced North-South power relations. By challenging established perspectives, this research contributes to transformative discourse and action, offering new pathways for understanding and combating corruption. These insights have significant theoretical and practical implications for improving the effectiveness of corruption prevention and counteraction strategies globally.

Employees’ loyalty is essential for improving the organization’s performance, thus aiding sustainable economic growth. The study examines the relationship between employee loyalty, organizational performance, and economic sustainability in Malaysian organizations. The results indicate a robust positive correlation between organizational performance and employee loyalty, suggesting loyalty drives productivity, profitability, and operational efficiency. Additionally, the study highlights organizational performance as a mediator that connects loyalty to aggregate-level economic consequences, such as resilience and adaptability under volatile market conditions. The research emphasizes the role of leadership, company culture, and work environments that support cultivating loyalty. It also highlights how loyal employees can be a cornerstone of innovation and corporate social responsibility, which aligns with Malaysia’s sustainable development agenda. By addressing this, organizations are encouraged to adopt measures that can foster loyalty and ensure long-term economic sustainability, including employee engagement initiatives, talent management, and recognition systems. Research to come should investigate longitudinal dynamics, cross-cultural comparisons, and sector-specific factors to cement a better base of understanding about the impact of employee loyalty on organizational and economic outcomes.